{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pretty_dataset_name(dataset_name):\n",
    "    if dataset_name == 'eth':\n",
    "        return 'ETH - Univ'\n",
    "    elif dataset_name == 'hotel':\n",
    "        return 'ETH - Hotel'\n",
    "    elif dataset_name == 'univ':\n",
    "        return 'UCY - Univ'\n",
    "    elif dataset_name == 'zara1':\n",
    "        return 'UCY - Zara 1'\n",
    "    elif dataset_name == 'zara2':\n",
    "        return 'UCY - Zara 2'\n",
    "    else:\n",
    "        return dataset_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_names = ['eth', 'hotel', 'univ', 'zara1', 'zara2', 'Average']\n",
    "alg_name = \"Ours\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Displacement Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_work_fse_results = {\n",
    "    'ETH - Univ': OrderedDict([('Linear', 2.94), ('Vanilla LSTM', 2.41), ('Social LSTM', 2.35), ('Social Attention', 3.74)]),\n",
    "    'ETH - Hotel': OrderedDict([('Linear', 0.72), ('Vanilla LSTM', 1.91), ('Social LSTM', 1.76), ('Social Attention', 2.64)]),\n",
    "    'UCY - Univ': OrderedDict([('Linear', 1.59), ('Vanilla LSTM', 1.31), ('Social LSTM', 1.40), ('Social Attention', 0.52)]),\n",
    "    'UCY - Zara 1': OrderedDict([('Linear', 1.21), ('Vanilla LSTM', 0.88), ('Social LSTM', 1.00), ('Social Attention', 2.13)]),\n",
    "    'UCY - Zara 2': OrderedDict([('Linear', 1.48), ('Vanilla LSTM', 1.11), ('Social LSTM', 1.17), ('Social Attention', 3.92)]),\n",
    "    'Average': OrderedDict([('Linear', 1.59), ('Vanilla LSTM', 1.52), ('Social LSTM', 1.54), ('Social Attention', 2.59)])\n",
    "}\n",
    "\n",
    "\n",
    "# These are for a prediction horizon of 12 timesteps.\n",
    "prior_work_ade_results = {\n",
    "    'ETH - Univ': OrderedDict([('Linear', 1.33), ('Vanilla LSTM', 1.09), ('Social LSTM', 1.09), ('Social Attention', 0.39)]),\n",
    "    'ETH - Hotel': OrderedDict([('Linear', 0.39), ('Vanilla LSTM', 0.86), ('Social LSTM', 0.79), ('Social Attention', 0.29)]),\n",
    "    'UCY - Univ': OrderedDict([('Linear', 0.82), ('Vanilla LSTM', 0.61), ('Social LSTM', 0.67), ('Social Attention', 0.20)]),\n",
    "    'UCY - Zara 1': OrderedDict([('Linear', 0.62), ('Vanilla LSTM', 0.41), ('Social LSTM', 0.47), ('Social Attention', 0.30)]),\n",
    "    'UCY - Zara 2': OrderedDict([('Linear', 0.77), ('Vanilla LSTM', 0.52), ('Social LSTM', 0.56), ('Social Attention', 0.33)]),\n",
    "    'Average': OrderedDict([('Linear', 0.79), ('Vanilla LSTM', 0.70), ('Social LSTM', 0.72), ('Social Attention', 0.30)])\n",
    "}\n",
    "\n",
    "linestyles = ['--', '-.', '-', ':']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_markers = 'X'\n",
    "marker_size = 7\n",
    "line_colors = ['#1f78b4','#33a02c','#fb9a99','#e31a1c']\n",
    "area_colors = ['#80CBE5','#ABCB51', '#F05F78']\n",
    "area_rgbs = list()\n",
    "for c in area_colors:\n",
    "    area_rgbs.append([int(c[i:i+2], 16) for i in (1, 3, 5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_attention_radius_3_fde_most_likely.csv\n",
      "results/hotel_attention_radius_3_fde_most_likely.csv\n",
      "results/univ_attention_radius_3_fde_most_likely.csv\n",
      "results/zara1_attention_radius_3_fde_most_likely.csv\n",
      "results/zara2_attention_radius_3_fde_most_likely.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>error_value</th>\n",
       "      <th>error_type</th>\n",
       "      <th>type</th>\n",
       "      <th>dataset</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.242668</td>\n",
       "      <td>fde</td>\n",
       "      <td>ml</td>\n",
       "      <td>eth</td>\n",
       "      <td>Ours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.158331</td>\n",
       "      <td>fde</td>\n",
       "      <td>ml</td>\n",
       "      <td>eth</td>\n",
       "      <td>Ours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.095482</td>\n",
       "      <td>fde</td>\n",
       "      <td>ml</td>\n",
       "      <td>eth</td>\n",
       "      <td>Ours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.069288</td>\n",
       "      <td>fde</td>\n",
       "      <td>ml</td>\n",
       "      <td>eth</td>\n",
       "      <td>Ours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.734359</td>\n",
       "      <td>fde</td>\n",
       "      <td>ml</td>\n",
       "      <td>eth</td>\n",
       "      <td>Ours</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   error_value error_type type dataset method\n",
       "0     0.242668        fde   ml     eth   Ours\n",
       "1     0.158331        fde   ml     eth   Ours\n",
       "2     0.095482        fde   ml     eth   Ours\n",
       "3     1.069288        fde   ml     eth   Ours\n",
       "4     1.734359        fde   ml     eth   Ours"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Ours\n",
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}*attention_radius_3*fde_most_likely.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']\n",
    "perf_df = perf_df.rename(columns={\"metric\": \"error_type\", \"value\": \"error_value\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Trajectron and GAN\n",
    "errors_df = pd.concat([pd.read_csv(f) for f in glob.glob('csv/old/curr_*_errors.csv')], ignore_index=True)\n",
    "del errors_df['data_precondition']\n",
    "errors_df = errors_df[~(errors_df['method'] == 'our_full')]\n",
    "errors_df = errors_df[~(errors_df['error_type'] == 'mse')]\n",
    "errors_df.loc[errors_df['error_type'] =='fse', 'error_type'] = 'fde'\n",
    "#errors_df.loc[errors_df['error_type'] =='mse', 'error_type'] = 'ade'\n",
    "errors_df.loc[errors_df['method'] == 'our_most_likely', 'method'] = 'Trajectron'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/pandas/core/frame.py:7123: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>method</th>\n",
       "      <th>run</th>\n",
       "      <th>node</th>\n",
       "      <th>sample</th>\n",
       "      <th>error_type</th>\n",
       "      <th>error_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2186000</td>\n",
       "      <td>hotel</td>\n",
       "      <td>sgan</td>\n",
       "      <td>0</td>\n",
       "      <td>Pedestrian/0</td>\n",
       "      <td>0</td>\n",
       "      <td>fde</td>\n",
       "      <td>4.045972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2186001</td>\n",
       "      <td>hotel</td>\n",
       "      <td>sgan</td>\n",
       "      <td>0</td>\n",
       "      <td>Pedestrian/0</td>\n",
       "      <td>1</td>\n",
       "      <td>fde</td>\n",
       "      <td>3.717624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2186002</td>\n",
       "      <td>hotel</td>\n",
       "      <td>sgan</td>\n",
       "      <td>0</td>\n",
       "      <td>Pedestrian/0</td>\n",
       "      <td>2</td>\n",
       "      <td>fde</td>\n",
       "      <td>5.378286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2186003</td>\n",
       "      <td>hotel</td>\n",
       "      <td>sgan</td>\n",
       "      <td>0</td>\n",
       "      <td>Pedestrian/0</td>\n",
       "      <td>3</td>\n",
       "      <td>fde</td>\n",
       "      <td>4.215567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2186004</td>\n",
       "      <td>hotel</td>\n",
       "      <td>sgan</td>\n",
       "      <td>0</td>\n",
       "      <td>Pedestrian/0</td>\n",
       "      <td>4</td>\n",
       "      <td>fde</td>\n",
       "      <td>4.663851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77099995</td>\n",
       "      <td>zara2</td>\n",
       "      <td>sgan</td>\n",
       "      <td>99</td>\n",
       "      <td>Pedestrian/35</td>\n",
       "      <td>1995</td>\n",
       "      <td>fde</td>\n",
       "      <td>0.620136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77099996</td>\n",
       "      <td>zara2</td>\n",
       "      <td>sgan</td>\n",
       "      <td>99</td>\n",
       "      <td>Pedestrian/35</td>\n",
       "      <td>1996</td>\n",
       "      <td>fde</td>\n",
       "      <td>0.681608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77099997</td>\n",
       "      <td>zara2</td>\n",
       "      <td>sgan</td>\n",
       "      <td>99</td>\n",
       "      <td>Pedestrian/35</td>\n",
       "      <td>1997</td>\n",
       "      <td>fde</td>\n",
       "      <td>0.860765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77099998</td>\n",
       "      <td>zara2</td>\n",
       "      <td>sgan</td>\n",
       "      <td>99</td>\n",
       "      <td>Pedestrian/35</td>\n",
       "      <td>1998</td>\n",
       "      <td>fde</td>\n",
       "      <td>0.545317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77099999</td>\n",
       "      <td>zara2</td>\n",
       "      <td>sgan</td>\n",
       "      <td>99</td>\n",
       "      <td>Pedestrian/35</td>\n",
       "      <td>1999</td>\n",
       "      <td>fde</td>\n",
       "      <td>1.027843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25700000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         dataset method  run           node  sample error_type  error_value\n",
       "2186000    hotel   sgan    0   Pedestrian/0       0        fde     4.045972\n",
       "2186001    hotel   sgan    0   Pedestrian/0       1        fde     3.717624\n",
       "2186002    hotel   sgan    0   Pedestrian/0       2        fde     5.378286\n",
       "2186003    hotel   sgan    0   Pedestrian/0       3        fde     4.215567\n",
       "2186004    hotel   sgan    0   Pedestrian/0       4        fde     4.663851\n",
       "...          ...    ...  ...            ...     ...        ...          ...\n",
       "77099995   zara2   sgan   99  Pedestrian/35    1995        fde     0.620136\n",
       "77099996   zara2   sgan   99  Pedestrian/35    1996        fde     0.681608\n",
       "77099997   zara2   sgan   99  Pedestrian/35    1997        fde     0.860765\n",
       "77099998   zara2   sgan   99  Pedestrian/35    1998        fde     0.545317\n",
       "77099999   zara2   sgan   99  Pedestrian/35    1999        fde     1.027843\n",
       "\n",
       "[25700000 rows x 7 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df = perf_df.append(errors_df)\n",
    "errors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.color_palette(\"muted\"):\n",
    "    fig_fse, ax_fses = plt.subplots(nrows=1, ncols=6, figsize=(8, 4), dpi=300, sharey=True)\n",
    "    for idx, ax_fse in enumerate(ax_fses):\n",
    "        dataset_name = dataset_names[idx]\n",
    "        if dataset_name != 'Average':\n",
    "            specific_df = perf_df[(perf_df['dataset'] == dataset_name) & (perf_df['error_type'] == 'fde')]\n",
    "            specific_df['dataset'] = pretty_dataset_name(dataset_name)\n",
    "        else:\n",
    "            specific_df = perf_df[(perf_df['error_type'] == 'fde')].copy()\n",
    "            specific_df['dataset'] = 'Average'\n",
    "\n",
    "        sns.boxplot(x='dataset', y='error_value', hue='method',\n",
    "            data=specific_df, ax=ax_fse, showfliers=False,\n",
    "            palette=area_colors, hue_order=['sgan', 'Trajectron', alg_name], width=2.)\n",
    "        \n",
    "        ax_fse.get_legend().remove()\n",
    "        ax_fse.set_xlabel('')\n",
    "        ax_fse.set_ylabel('' if idx > 0 else 'Final Displacement Error (m)')\n",
    "\n",
    "        ax_fse.scatter([-0.665, 0, 0.665],\n",
    "               [np.mean(specific_df[specific_df['method'] == 'sgan']['error_value']),\n",
    "                np.mean(specific_df[specific_df['method'] == 'Trajectron']['error_value']),\n",
    "                np.mean(specific_df[specific_df['method'] == alg_name]['error_value'])],\n",
    "               s=marker_size*marker_size, c=np.asarray(area_rgbs)/255.0, marker=mean_markers,\n",
    "               edgecolors='#545454', zorder=10)\n",
    "        \n",
    "        for baseline_idx, (baseline, fse_val) in enumerate(prior_work_fse_results[pretty_dataset_name(dataset_name)].items()):\n",
    "            ax_fse.axhline(y=fse_val, label=baseline, color=line_colors[baseline_idx], linestyle=linestyles[baseline_idx])\n",
    "            \n",
    "        if idx == 0:\n",
    "            handles, labels = ax_fse.get_legend_handles_labels()\n",
    "\n",
    "\n",
    "            handles = [handles[0], handles[4], handles[1], handles[5], handles[2], handles[6], handles[3]]\n",
    "            labels = [labels[0], 'Social GAN', labels[1], 'Trajectron', labels[2], alg_name, labels[3]]\n",
    "\n",
    "            ax_fse.legend(handles, labels, \n",
    "                          loc='lower center', bbox_to_anchor=(0.5, 0.9),\n",
    "                          ncol=4, borderaxespad=0, frameon=False,\n",
    "                          bbox_transform=fig_fse.transFigure)\n",
    "\n",
    "\n",
    "#     fig_fse.text(0.51, 0.03, 'Dataset', ha='center')\n",
    "\n",
    "plt.savefig('plots/fde_boxplots.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df\n",
    "del errors_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average Displacement Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_attention_radius_3_ade_most_likely.csv\n",
      "results/hotel_attention_radius_3_ade_most_likely.csv\n",
      "results/univ_attention_radius_3_ade_most_likely.csv\n",
      "results/zara1_attention_radius_3_ade_most_likely.csv\n",
      "results/zara2_attention_radius_3_ade_most_likely.csv\n"
     ]
    }
   ],
   "source": [
    "# Load Ours\n",
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}*attention_radius_3*ade_most_likely.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']\n",
    "perf_df = perf_df.rename(columns={\"metric\": \"error_type\", \"value\": \"error_value\"})\n",
    "#perf_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Trajectron and GAN\n",
    "errors_df = pd.concat([pd.read_csv(f) for f in glob.glob('old/curr_*_errors.csv')], ignore_index=True)\n",
    "del errors_df['data_precondition']\n",
    "errors_df = errors_df[~(errors_df['method'] == 'our_full')]\n",
    "errors_df = errors_df[~(errors_df['error_type'] == 'fse')]\n",
    "#errors_df.loc[errors_df['error_type'] =='fse', 'error_type'] = 'fde'\n",
    "errors_df.loc[errors_df['error_type'] =='mse', 'error_type'] = 'ade'\n",
    "errors_df.loc[errors_df['method'] == 'our_most_likely', 'method'] = 'Trajectron'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/pandas/core/frame.py:7123: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort,\n"
     ]
    }
   ],
   "source": [
    "perf_df = perf_df.append(errors_df)\n",
    "del errors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.color_palette(\"muted\"):\n",
    "    fig_fse, ax_fses = plt.subplots(nrows=1, ncols=6, figsize=(8, 4), dpi=300, sharey=True)\n",
    "    for idx, ax_fse in enumerate(ax_fses):\n",
    "        dataset_name = dataset_names[idx]\n",
    "        if dataset_name != 'Average':\n",
    "            specific_df = perf_df[(perf_df['dataset'] == dataset_name) & (perf_df['error_type'] == 'ade')]\n",
    "            specific_df['dataset'] = pretty_dataset_name(dataset_name)\n",
    "        else:\n",
    "            specific_df = perf_df[(perf_df['error_type'] == 'ade')].copy()\n",
    "            specific_df['dataset'] = 'Average'\n",
    "\n",
    "        sns.boxplot(x='dataset', y='error_value', hue='method',\n",
    "            data=specific_df, ax=ax_fse, showfliers=False,\n",
    "            palette=area_colors, hue_order=['sgan', 'Trajectron', alg_name], width=2.)\n",
    "\n",
    "        ax_fse.get_legend().remove()\n",
    "        ax_fse.set_xlabel('')\n",
    "        ax_fse.set_ylabel('' if idx > 0 else 'Average Displacement Error (m)')\n",
    "\n",
    "        ax_fse.scatter([-0.665, 0, 0.665],\n",
    "               [np.mean(specific_df[specific_df['method'] == 'sgan']['error_value']),\n",
    "                np.mean(specific_df[specific_df['method'] == 'Trajectron']['error_value']),\n",
    "                np.mean(specific_df[specific_df['method'] == alg_name]['error_value'])],\n",
    "               s=marker_size*marker_size, c=np.asarray(area_rgbs)/255.0, marker=mean_markers,\n",
    "               edgecolors='#545454', zorder=10)\n",
    "        \n",
    "        for baseline_idx, (baseline, fse_val) in enumerate(prior_work_ade_results[pretty_dataset_name(dataset_name)].items()):\n",
    "            ax_fse.axhline(y=fse_val, label=baseline, color=line_colors[baseline_idx], linestyle=linestyles[baseline_idx])\n",
    "            \n",
    "        if idx == 0:\n",
    "            handles, labels = ax_fse.get_legend_handles_labels()\n",
    "\n",
    "\n",
    "            handles = [handles[0], handles[4], handles[1], handles[5], handles[2], handles[6], handles[3]]\n",
    "            labels = [labels[0], 'Social GAN', labels[1], 'Trajectron', labels[2], alg_name, labels[3]]\n",
    "\n",
    "            ax_fse.legend(handles, labels, \n",
    "                          loc='lower center', bbox_to_anchor=(0.5, 0.9),\n",
    "                          ncol=4, borderaxespad=0, frameon=False,\n",
    "                          bbox_transform=fig_fse.transFigure)\n",
    "\n",
    "#     fig_fse.text(0.51, 0.03, 'Dataset', ha='center')\n",
    "\n",
    "plt.savefig('plots/ade_boxplots.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KDE Negative Log Likelihood Attention Radius 3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_12_kde_full.csv\n",
      "results/hotel_12_kde_full.csv\n",
      "results/univ_12_kde_full.csv\n",
      "results/zara1_12_kde_full.csv\n",
      "results/zara2_12_kde_full.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/borisi/anaconda3/envs/gentraj/lib/python3.6/site-packages/pandas/core/frame.py:7123: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort,\n"
     ]
    }
   ],
   "source": [
    "# Load Ours\n",
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_12*kde_full.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True)\n",
    "        del perf_df['Unnamed: 0']\n",
    "#perf_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Load Trajectron and SGAN\n",
    "# lls_df = pd.concat([pd.read_csv(f) for f in glob.glob('csv/old/curr_*_lls.csv')], ignore_index=True)\n",
    "# lls_df.loc[lls_df['method'] == 'our_full', 'method'] = 'Trajectron'\n",
    "# lls_df['error_type'] = 'KDE'\n",
    "# #lls_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KDE NLL for ETH - Univ\n",
      "Ours: 1.3068431681049446\n",
      "KDE NLL for ETH - Hotel\n",
      "Ours: -1.939345347471224\n",
      "KDE NLL for UCY - Univ\n",
      "Ours: -1.1288059163920086\n",
      "KDE NLL for UCY - Zara 1\n",
      "Ours: -1.4119791272274707\n",
      "KDE NLL for UCY - Zara 2\n",
      "Ours: -2.525154634369542\n",
      "KDE NLL for Average\n",
      "Ours: -1.392358401395975\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    if dataset != 'Average':\n",
    "        print('KDE NLL for ' + pretty_dataset_name(dataset))\n",
    "        #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan') & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n",
    "        #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron')  & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print('KDE NLL for ' + pretty_dataset_name(dataset))\n",
    "        #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan')]['log-likelihood'].mean()}\")\n",
    "        #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron')]['log-likelihood'].mean()}\")\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Likely FDE Attention Radius 3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_12_fde_most_likely.csv\n",
      "results/hotel_12_fde_most_likely.csv\n",
      "results/univ_12_fde_most_likely.csv\n",
      "results/zara1_12_fde_most_likely.csv\n",
      "results/zara2_12_fde_most_likely.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_12*fde_most_likely.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = 'Trajectron++'\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDE Most Likely for ETH - Univ\n",
      "Ours: 1.6763703267322265\n",
      "FDE Most Likely for ETH - Hotel\n",
      "Ours: 0.4614785391399073\n",
      "FDE Most Likely for UCY - Univ\n",
      "Ours: 1.0747924515297491\n",
      "FDE Most Likely for UCY - Zara 1\n",
      "Ours: 0.7704666189102252\n",
      "FDE Most Likely for UCY - Zara 2\n",
      "Ours: 0.5865659029486421\n",
      "FDE Most Likely for Average\n",
      "Ours: 0.9542581296327649\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('FDE Most Likely for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Likely ADE Attention Radius 3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_12_ade_most_likely.csv\n",
      "results/hotel_12_ade_most_likely.csv\n",
      "results/univ_12_ade_most_likely.csv\n",
      "results/zara1_12_ade_most_likely.csv\n",
      "results/zara2_12_ade_most_likely.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_12*ade_most_likely.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = 'Trajectron++'\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADE Most Likely for ETH - Univ\n",
      "Ours: 0.7052341524825474\n",
      "ADE Most Likely for ETH - Hotel\n",
      "Ours: 0.21620184785291033\n",
      "ADE Most Likely for UCY - Univ\n",
      "Ours: 0.40926643885853664\n",
      "ADE Most Likely for UCY - Zara 1\n",
      "Ours: 0.2972134362490682\n",
      "ADE Most Likely for UCY - Zara 2\n",
      "Ours: 0.22585898118058487\n",
      "ADE Most Likely for Average\n",
      "Ours: 0.3661968268243691\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('ADE Most Likely for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best of 20 Evaluation FDE Attention Radius 3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_12_fde_best_of.csv\n",
      "results/hotel_12_fde_best_of.csv\n",
      "results/univ_12_fde_best_of.csv\n",
      "results/zara1_12_fde_best_of.csv\n",
      "results/zara2_12_fde_best_of.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_12*fde_best_of.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDE Best of 20 for ETH - Univ\n",
      "Trajectron++: 0.8574431969131285\n",
      "FDE Best of 20 for ETH - Hotel\n",
      "Trajectron++: 0.19084707198210932\n",
      "FDE Best of 20 for UCY - Univ\n",
      "Trajectron++: 0.4287221576716801\n",
      "FDE Best of 20 for UCY - Zara 1\n",
      "Trajectron++: 0.3159092794417088\n",
      "FDE Best of 20 for UCY - Zara 2\n",
      "Trajectron++: 0.25292433731989494\n",
      "FDE Best of 20 for Average\n",
      "Trajectron++: 0.38676102425417497\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best of 20 Evaluation ADE Attention Radius 3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_12_ade_best_of.csv\n",
      "results/hotel_12_ade_best_of.csv\n",
      "results/univ_12_ade_best_of.csv\n",
      "results/zara1_12_ade_best_of.csv\n",
      "results/zara2_12_ade_best_of.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_12*ade_best_of.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADE Best of 20 for ETH - Univ\n",
      "Trajectron++: 0.4334745882075259\n",
      "ADE Best of 20 for ETH - Hotel\n",
      "Trajectron++: 0.12103208674836993\n",
      "ADE Best of 20 for UCY - Univ\n",
      "Trajectron++: 0.2200696369591878\n",
      "ADE Best of 20 for UCY - Zara 1\n",
      "Trajectron++: 0.16745747931164434\n",
      "ADE Best of 20 for UCY - Zara 2\n",
      "Trajectron++: 0.12485689651978099\n",
      "ADE Best of 20 for Average\n",
      "Trajectron++: 0.1987725413014945\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('ADE Best of 20 for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KDE Negative Log Likelihood Attention Radius 3m Velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_vel_12_kde_full.csv\n",
      "results/hotel_vel_12_kde_full.csv\n",
      "results/univ_vel_12_kde_full.csv\n",
      "results/zara1_vel_12_kde_full.csv\n",
      "results/zara2_vel_12_kde_full.csv\n"
     ]
    }
   ],
   "source": [
    "# Load Ours\n",
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_vel_12*kde_full.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True)\n",
    "        del perf_df['Unnamed: 0']\n",
    "#perf_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Load Trajectron and SGAN\n",
    "# lls_df = pd.concat([pd.read_csv(f) for f in glob.glob('csv/old/curr_*_lls.csv')], ignore_index=True)\n",
    "# lls_df.loc[lls_df['method'] == 'our_full', 'method'] = 'Trajectron'\n",
    "# lls_df['error_type'] = 'KDE'\n",
    "# #lls_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KDE NLL for ETH - Univ\n",
      "Ours: 1.7987703559884305\n",
      "KDE NLL for ETH - Hotel\n",
      "Ours: -1.2864991518790894\n",
      "KDE NLL for UCY - Univ\n",
      "Ours: -0.8897570611371921\n",
      "KDE NLL for UCY - Zara 1\n",
      "Ours: -1.1275849983603234\n",
      "KDE NLL for UCY - Zara 2\n",
      "Ours: -2.1946898640072234\n",
      "KDE NLL for Average\n",
      "Ours: -1.1171728799903844\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    if dataset != 'Average':\n",
    "        print('KDE NLL for ' + pretty_dataset_name(dataset))\n",
    "        #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan') & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n",
    "        #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron')  & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print('KDE NLL for ' + pretty_dataset_name(dataset))\n",
    "        #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan')]['log-likelihood'].mean()}\")\n",
    "        #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron')]['log-likelihood'].mean()}\")\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Likely FDE Attention Radius 3m Velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_vel_12_fde_most_likely.csv\n",
      "results/hotel_vel_12_fde_most_likely.csv\n",
      "results/univ_vel_12_fde_most_likely.csv\n",
      "results/zara1_vel_12_fde_most_likely.csv\n",
      "results/zara2_vel_12_fde_most_likely.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_vel_12*fde_most_likely.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = 'Trajectron++'\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDE Most Likely for ETH - Univ\n",
      "Ours: 1.6595803163079856\n",
      "FDE Most Likely for ETH - Hotel\n",
      "Ours: 0.4577661486145857\n",
      "FDE Most Likely for UCY - Univ\n",
      "Ours: 1.1657061102834114\n",
      "FDE Most Likely for UCY - Zara 1\n",
      "Ours: 0.792450220754066\n",
      "FDE Most Likely for UCY - Zara 2\n",
      "Ours: 0.5878910318410495\n",
      "FDE Most Likely for Average\n",
      "Ours: 1.0204553297895693\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('FDE Most Likely for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Likely Evaluation ADE Attention Radius 3m Velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_vel_12_ade_most_likely.csv\n",
      "results/hotel_vel_12_ade_most_likely.csv\n",
      "results/univ_vel_12_ade_most_likely.csv\n",
      "results/zara1_vel_12_ade_most_likely.csv\n",
      "results/zara2_vel_12_ade_most_likely.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_vel_12*ade_most_likely.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = 'Trajectron++'\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADE Most Likely for ETH - Univ\n",
      "Ours: 0.7081645037230024\n",
      "ADE Most Likely for ETH - Hotel\n",
      "Ours: 0.21800273126936145\n",
      "ADE Most Likely for UCY - Univ\n",
      "Ours: 0.4429943056454118\n",
      "ADE Most Likely for UCY - Zara 1\n",
      "Ours: 0.30200377722385563\n",
      "ADE Most Likely for UCY - Zara 2\n",
      "Ours: 0.22635933788153614\n",
      "ADE Most Likely for Average\n",
      "Ours: 0.3907335607353219\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('ADE Most Likely for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best of 20 Evaluation FDE Attention Radius 3m Velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_vel_12_fde_best_of.csv\n",
      "results/hotel_vel_12_fde_best_of.csv\n",
      "results/univ_vel_12_fde_best_of.csv\n",
      "results/zara1_vel_12_fde_best_of.csv\n",
      "results/zara2_vel_12_fde_best_of.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_vel_12*fde_best_of.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDE Best of 20 for ETH - Univ\n",
      "Trajectron++: 0.8281783496158873\n",
      "FDE Best of 20 for ETH - Hotel\n",
      "Trajectron++: 0.20567153121359805\n",
      "FDE Best of 20 for UCY - Univ\n",
      "Trajectron++: 0.44246075609459684\n",
      "FDE Best of 20 for UCY - Zara 1\n",
      "Trajectron++: 0.3307553133179889\n",
      "FDE Best of 20 for UCY - Zara 2\n",
      "Trajectron++: 0.24909920986736078\n",
      "FDE Best of 20 for Average\n",
      "Trajectron++: 0.39711722810872235\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best of 20 Evaluation ADE Attention Radius 3m Velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/eth_vel_12_ade_best_of.csv\n",
      "results/hotel_vel_12_ade_best_of.csv\n",
      "results/univ_vel_12_ade_best_of.csv\n",
      "results/zara1_vel_12_ade_best_of.csv\n",
      "results/zara2_vel_12_ade_best_of.csv\n"
     ]
    }
   ],
   "source": [
    "perf_df = pd.DataFrame()\n",
    "for dataset in dataset_names:\n",
    "    for f in glob.glob(f\"results/{dataset}_vel_12*ade_best_of.csv\"):\n",
    "        print(f)\n",
    "        dataset_df = pd.read_csv(f)\n",
    "        dataset_df['dataset'] = dataset\n",
    "        dataset_df['method'] = alg_name\n",
    "        perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n",
    "        del perf_df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADE Best of 20 for ETH - Univ\n",
      "Trajectron++: 0.39238413417612106\n",
      "ADE Best of 20 for ETH - Hotel\n",
      "Trajectron++: 0.11769507047457346\n",
      "ADE Best of 20 for UCY - Univ\n",
      "Trajectron++: 0.1990357831124613\n",
      "ADE Best of 20 for UCY - Zara 1\n",
      "Trajectron++: 0.15218487860383714\n",
      "ADE Best of 20 for UCY - Zara 2\n",
      "Trajectron++: 0.11350252815738102\n",
      "ADE Best of 20 for Average\n",
      "Trajectron++: 0.1802170043575296\n"
     ]
    }
   ],
   "source": [
    "for dataset in dataset_names:\n",
    "    print('ADE Best of 20 for ' + pretty_dataset_name(dataset))\n",
    "    if dataset != 'Average':\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
    "    else:\n",
    "        print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "del perf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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